Clostridium difficile Drug Metabolism Modeling

Background

C. difficile can utilize heme from the host to protect against antibiotic therapy and immune cell mediated oxidative stress produced at the host-pathogen interface. HatRT system in C. difficile senses and detoxifies excess intracellular heme through efflux. The results herein further refine our current model of heme homeostasis in C. difficile, Figure 1.

HsmR senses low concentrations of heme and activates expression of the hsmRA operon which leads to the integration of heme into HsmA. Heme-bound HsmA within the membrane shields the bacterium against redox active molecules. Concurrently, HatR binds heme, derepressing the hatRT operon, leading to subsequent efflux of free heme through HatT, and resulting in a relief from heme toxicity. Together these systems function to maintain a tolerable concentration of intracellular heme for C. difficile to protect itself against the stressors encountered within the host during CDI [2].

Adding target reactions to the model

Starting from the published C. difficile genome-scale metabolic networks CD196 We inserted heam transport reactions as reversible (while originally C. bifermentans metabolic model they were compiled as irreversible). Referencing to the script Building_Model.m. It will apply the method addReactionGEM_Unito.m.

The CDI infection model

The CDI infection Petri Net model is composed by places (graphically represented by circles) corresponding to epithelial cells, bacterial biomass, metabolites and tissue state (i.e. damage to the colonic mucosa) and by transitions (graphically represented by rectangles) corresponding to the interactions among the entities, cellular death, intake or efflux of metabolites, toxin action, intestinal inflammation and drug activity.

The model is composed by five modules: IECs, LumenEnv, FbaEnv, Drug, BloodVessel.

Testing CD196SinkHeme model alone

Lets first compile the metabolic model from the .RData storing the chemical reaction network, and then generate a flux distribution to test the optimization of the mathematical programming object.

We exploited flux_balance() method to perform sensitivity analysis acting on the permissible flux through target reactions (flux distribution is obtained through running parsimonious FBA or minimum total flux (MTF)).

We also exploited flux_balance_fba() method to perform sensitivity analysis acting on the permissible flux through target reactions (flux distribution is obtained through running FBA).

Also, we evaluate the estimanted flux solving LPs through GLPK called within epimod framework.

FBA solution space is explored by Boundary Reactions bounds variation.

Apparently reactions “EX_leu_L(e)” and “EX_pro_L(e)” and “EX_val_L(e)” showed unexpected behaviour. EX_pro_L(e) is always 0 whereas EX_leu_L(e) is always positive. Therefore, we are going to expore the solution ladscape for “EX_leu_L(e)” and “EX_pro_L(e)” “EX_val_L(e)” reactions flux estimations.

Testing CD196SinkHeme and diet

CD196SinkHeme genome-scale metabolic model is considered in the final model exposed to a defined metabolic environment. The metabolomic landscape available to bacteria is given by a diet formulation. Therefore, from the bacteria perspective, the dietary input compartment represents the exchange medium consisting of all the dietary ingredients that C. difficile can consume. This step focuses on adjusting the exchange reactions linked to the metabolite identifiers.

diets.RData is generated following the Script4Diets.R. Starting from diet data download from the repository of Virtual Metabolic Human, we then convert fluxed expressed as [flux in mmol/human day] to [flux in mmol/human h]. We assumed that this flux is considered as metabolites concentrations as steady state [mmol], i.e. the maximal uptake can be constrained by restricting the lower bounds.

We exploited flux_balance_fba() method to perform sensitivity analysis acting on the permissible flux through target reactions (flux distribution is obtained through running FBA).

We will obtain fluxes solutions considering a medium metabolites concentrations equal to the lower bounds contraints of the published model. Solution are estimated given EX_biomass(e) reactions’s upper bounds equal that of the published model. Lower bounds sets associated to metaboliutes not envised in the diet formulation are kept unvaried.

Based on amino acid utilization, the total consumption of nitrogen is between the interval of 10 [mmol/gDW h] and 0.1 [mmol/gDW h]

HYD4 flux distribution

HYD4 flux distribution solutions produced from rnorm bounds distribution (n = 5000)

Appendix A: Looking for model boundary reactions

There are three different types of pre-defined boundary reactions:

Exchange. An exchange reaction is a reversible reaction that adds to or removes an extracellular metabolite from the extracellular compartment.

Example of exchange reactions:

Demand. A demand reaction is an irreversible reaction that consumes an intracellular metabolite.

Example of demand reactions:

Sink. A sink is similar to an exchange but specifically for intracellular metabolites, i.e., a reversible reaction that adds or removes an intracellular metabolite.

Example of demand reactions:

All of them are unbalanced pseudo reactions, that means they fulfill a function for modeling by adding to or removing metabolites from the model system but are not based on real biology.